This paper explores the correlation between linguistic diversity, sentiment
analysis and transformer model architectures. We aim to investigate how
different English variations impact transformer-based models for irony
detection. To conduct our study, we used the EPIC corpus to extract five
diverse English variation-specific datasets and applied the KEN pruning
algorithm on five different architectures. Our results reveal several
similarities between optimal subnetworks, which provide insights into the
linguistic variations that share strong resemblances and those that exhibit
greater dissimilarities. We discovered that optimal subnetworks across models
share at least 60% of their parameters, emphasizing the significance of
parameter values in capturing and interpreting linguistic variations. This
study highlights the inherent structural similarities between models trained on
different variants of the same language and also the critical role of parameter
values in capturing these nuances.

本研究探讨语言多样性、情感分析和变压器模型体系结构之间的相关性，旨在调查不同的英语变体对于基于变压器模型的讽刺检测的影响。我们使用 EPIC 语料库提取了五个不同的英语变体特定数据集，并在五个不同的体系结构上应用了 KEN 修剪算法，从而进行了研究。我们的结果揭示了最佳子网络之间的几个相似之处，这些相似之处提供了对共享强烈相似之处和表现出更大差异的语言变体的见解。我们发现，跨模型的最佳子网络至少共享 60% 的参数，强调了参数值在捕捉和解释语言变化方面的重要性。本研究突显了在不同语言变体上训练的模型之间固有的结构相似性，并强调了参数值在捕捉这些细微差别中的关键作用。